Abstract
Image categorization is the process of assigning land cover classes to pixels. It may categorize images into forest, urban, agricultural, and other categories. The approaches in this study are tested using a large image dataset comprising 21 land use categories. There are comparisons to be done in addition to traditional approaches. DWT at two degrees of decomposition is used to extract texture features from remote sensing images. The results are explained using the UC Merced dataset. At the approximation sub-band, 72 features are extracted at the second level of decomposition using DWT and other feature extraction methods. The data is classified using the supervised support vector machine (SVM) approach, and the results with the highest accuracy are determined.
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Prabhakar, T., Srujan Raju, K., Reddy Madhavi, K. (2022). Support Vector Machine Classification of Remote Sensing Images with the Wavelet-based Statistical Features. In: Satapathy, S.C., Bhateja, V., Favorskaya, M.N., Adilakshmi, T. (eds) Smart Intelligent Computing and Applications, Volume 2. Smart Innovation, Systems and Technologies, vol 283. Springer, Singapore. https://doi.org/10.1007/978-981-16-9705-0_59
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DOI: https://doi.org/10.1007/978-981-16-9705-0_59
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